EasyDock 1.3: An Automated Pipeline for Molecular Docking

Area of Science:

• Computational Chemistry
• Drug Discovery
• Bioinformatics

Background:

• Molecular docking is crucial for screening large compound libraries in drug design.
• Previous versions of EasyDock offered automated docking with multiserver capabilities.
• The need for broader engine support and enhanced preparation methods was identified.

Purpose of the Study:

• To present an extended version of the EasyDock automated molecular docking pipeline.
• To incorporate a wider range of docking engines, including deep-learning-based ones.
• To enhance ligand preparation and postdocking analysis functionalities.

Main Methods:

• Integrated Vina-family (CPU/GPU) and deep-learning docking engines (CarsiDock, SurfDock) via client-server architecture.
• Enriched ligand preparation with salt stripping, stereoisomer enumeration, and conformational sampling.
• Incorporated open-source protonation tools (pkasolver, MolGpKa, Uni-pKa) and postdocking analysis (PLIF, PoseBusters).

Main Results:

• The extended EasyDock pipeline now supports a broader spectrum of docking engines.
• Advanced ligand preparation and analysis tools are integrated, replacing commercial software.
• Apptainer/Docker containers facilitate simplified installation and deployment on HPC systems.

Conclusions:

• The enhanced EasyDock pipeline offers a comprehensive, open-source solution for large-scale molecular docking.
• Its expanded capabilities and ease of deployment accelerate computational drug discovery workflows.
• The fully open-source nature promotes accessibility and further development in the field.